A prototype transistor built from molybdenum disulphide and carbon nanotubes rather than silicon could allow power-hungry AIs to run on smartwatches without rapidly draining the battery
A reconfigurable transistor can run AI processes using 100 times less electricity than the standard transistors found in silicon-based chips. It could help spur development of a new generation of smartwatches or other wearable devices capable of using powerful AI technology – something that is impractical today because many AI algorithms would rapidly drain the batteries of wearables built with ordinary transistors.
The new transistors are made of molybdenum disulphide and carbon nanotubes. They can be continuously reconfigured by electric fields to almost instantaneously handle multiple steps in AI-driven processes. In contrast, silicon-based transistors – which act as tiny on-or-off electronic switches – can only perform one step at a time.
“The low energy results from the fact that we can implement the with a 100-fold reduction in the number of transistors, compared to conventional silicon technology,” saysHersam and his colleagues demonstrated how the reconfigurable transistors can help a standard machine-learning-based AI algorithm interpret heartbeat data from 10,000 electrocardiogram tests.
But the researchers are still figuring out how to go beyond creating a few reconfigurable transistors for lab experiments. They hope to eventually mass-produce such transistors using standard
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